Implementation details.
All the values are obtained running the training with the same hardware and hyper-parameters.
When available, the original code released by the authors is used.
Our code is developed on top of the Unbiased Teacher [1]} source code.
We perform the training on a single machine with 6 Tesla P100 GPUs with 12GB of memory.
The train lasts 180,000 iterations with a batch size of 2 images per GPU for the supervised part and 2 images for the unsupervised part, with \(\alpha \)  set to 4.
We use the Stochastic Gradient Descent (SGD) optimization algorithm with a learning rate of 0.0075, a weight decay of 0.0001, and a momentum of 0.9.
The learning rate decays at iteration 179990 and 179995.
We use the Faster R-CNN with FPN [2]} and the ResNet 50 [3]} backbone for the teacher and student models, initialized by pre-trained on ImageNet, and the same augmentation of Unbiased Teacher [1]}.
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